We conduct experiments in a CMDP extension of the standard PyBullet tasks [1]} that is inspired by the multi-task reinforcement learning setups of [2]}. In these tasks, the agent is rewarded for running at a particular velocity that is randomly sampled at the beginning of each episode. We train expert policies that have access to this privileged information. We compare two algorithms: the off-policy behavioral cloning (BC) [3]} and the on-policy DAgger (DAgger) [4]} that either have access to the immediate state or the last five timesteps of history (for which we use the suffix -SEQ).
